Most attention mechanisms use the mapping of local or global contextual information to directly polish all pixels of the input, which can intensify the spread of valuable information and make the network pay more attention to meaningful features. For example, SE-Net [1]} is a representative attention mechanism which extracts global average pooling (GAP) of every channel as contextual information and recalibrates the input with the embedding of the contextual information, which can zoom in on the meaningful features. Different from SE-Net [1]}, GE-Net [3]} focuses on local contextual information and uses its mapping to recalibrate the input, which is a more general style. Similar to GE-Net [3]}, SPA-Net [5]} also considers local feature responses but from three different spatial scales to extract contextual information. In addition to the extraction of contextual information from the channel dimension performed by SPA-Net [5]}, CBAM [7]} and BAM [8]} extract contextual information from the extra spatial dimension and merge them, then recalibrate the input with the merged result.
